--- title: "Implementation Guide: Automate Manual Interventions on CNC Machines and Stream Events to ERP/MES" description: Step-by-step guide for CNC shops to automate manual interventions and stream machine events into ERP/MES to increase throughput and accuracy. image: https://www.jitbase.com/hubfs/blog-images/automate-manual-interventions-cnc-erp-mes-integration-hero.jpg --- [+1 438 942-8501](tel:+1%20438%20942-8501) [Log In](https://app.jitbase.com/) - [English](https://www.jitbase.com/blog/automate-manual-interventions-cnc-erp-mes-integration) - [Français](https://www.jitbase.com/fr/blog/suivi-production-cnc-automatisation-erp-mes-kpi) [![JITbase black logo](https://www.jitbase.com/hs-fs/hubfs/Logos/jitbase-logo-black-horiz.webp?width=175&height=88&name=jitbase-logo-black-horiz.webp)](https://www.jitbase.com) - Solutions Back ##### [Machine Monitoring](https://www.jitbase.com/machine-monitoring) - ##### [Machine Monitoring](https://www.jitbase.com/machine-monitoring) Back - [Live Dashboard](https://www.jitbase.com/machine-monitoring) - 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[English](https://www.jitbase.com/blog/automate-manual-interventions-cnc-erp-mes-integration) - [Français](https://www.jitbase.com/fr/blog/suivi-production-cnc-automatisation-erp-mes-kpi)   [Book a demo](https://www.jitbase.com/demo) [Connect Machines for Free](https://sso.jitbase.com/realms/webapp/protocol/openid-connect/registrations?client_id=website-registration&response_type=code&scope=openid&redirect_uri=https://admin.jitbase.com) ![CNC machine with an operator and an ERP dashboard: automated events replacing manual interventions with JITbase](https://www.jitbase.com/hubfs/blog-images/automate-manual-interventions-cnc-erp-mes-integration-hero.jpg) Blog [Production Management](https://www.jitbase.com/blog/tag/production-management) # Implementation Guide: Automate Manual Interventions on CNC Machines and Stream Events to ERP/MES [*written by* Judicael Deguenon on March 19, 2026](https://www.jitbase.com/blog/author/judicael-deguenon) Many small-to-medium CNC shops waste machine time and scheduling accuracy because machinists must stop programs, confirm tool changes, log offsets, or press start/stop on operator terminals. This guide shows how to automate manual interventions and implement a reliable pipeline to stream machine events into ERP/MES systems, so shops can increase throughput, reduce operator workload, and capture accurate cycle times. It includes architecture options, data capture methods, a pilot-to-rollout roadmap, KPI definitions, and common failure modes. The primary goal is to help operations teams automate manual interventions and connect CNC machines to their ERP/MES with practical steps, sample payloads, and measurable success criteria. **TL;DR:** - Start with a 1–3 machine pilot: expect 10–30% throughput improvement on targeted jobs and capture cycle times within 5–10% of validated time studies. - Prioritize high-frequency, high-impact events (tool changes, pallet swaps, cycle start/stops) and use an edge connector + MQTT/REST to push events into ERP/MES for low latency and auditability. - Next steps: run a two-week intervention log, pick 1–3 machines for a pilot, and define success criteria (manual interventions reduced, cycle time variance reduced, event reconciliation rate ≥ 98%). ## Why automate manual interventions on CNC machines? ### Business pain and operational impact Operator pauses for tool change confirmations, part loading, pallet swaps, probe actions, and manual cycle confirmations often account for 5–20% of apparent machine time on high-mix shops. Studies and shop-floor measurements show that targeted automation can recover 10–30% of productive machine hours depending on part mix and automation scope. That reclaimed capacity directly reduces lead time and improves on-time delivery without adding headcount. Workforce pressures are real (see trends on machine-tool occupations and staffing at the Bureau of Labor Statistics), and automation helps offset those constraints by reducing the number of manual decisions an operator must make each shift. For related workforce guidance, review our [machinist shortage guide](https://www.jitbase.com/blog/how-to-overcome-the-machinist-shortage-and-boost-production-capacity). ### Throughput, operator workload, and accuracy gains Automating manual interventions typically yields three measurable outcomes: - Increased effective machine utilization: more cycles completed per shift by removing operator wait times. - Reduced operator cognitive load: fewer confirmations and fewer chances for human error. - Improved data fidelity: accurate cycle time capture and automatic event logging reduces scheduling errors and planning guesswork. Define measurable objectives before starting: percent reduction in manual stops, target cycle time accuracy (within 5–10% of a validated time study), and event delivery success rate to ERP/MES (≥ 98%). These targets make pilots objective and easy to evaluate. ## Identify and prioritize manual interventions to automate ### Common intervention types on CNC machines Common on-machine interventions that are candidates for automation include: - Tool change confirmations and manual offset entry - Part load/unload and pallet swaps - Probe-triggered inspection and touch-off confirmations - Cycle start/stop and program end confirmations (M30, M99) - Operator reset inputs and cycle hold/resume actions - Program edits and feed overrides requiring operator sign-off Collect examples from your shop by logging each intervention over two weeks and tagging them by type, machine, operator, and program. ### How to score and prioritize interventions Use a simple prioritization matrix: Impact × Frequency / Implementation Cost. Score each intervention 1–5 on: - Impact on throughput or quality - Frequency per shift - Estimated engineering and hardware cost to automate High-impact, high-frequency, low-cost items should be automated first (for example, automatic cycle-stop logging and pallet presence detection). Medium-impact items (operator confirmations for certain tool offsets) can follow when the pilot proves event reliability. Gather baseline data using the [manual interventions checklist](https://www.jitbase.com/blog/reduce-manual-interventions-shop-floor-checklist) to prepare teams and document existing behavior. ## Technical architectures for streaming CNC events to ERP/MES (comparison table) Before choosing an architecture, map desired events (cycle\_start, cycle\_end, tool\_change, pallet\_swap, probe\_event) and required fields (machine\_id, program\_name, operator\_id, timestamp, cycle\_time\_ms). Typical connector protocols include MTConnect, OPC UA, MQTT, and REST APIs. Hardware commonly used: industrial edge PCs, protocol converters, small industrial gateways, and current clamps for spindle telemetry. ### Edge connector approach (on-machine edge device) - Deploy an industrial edge PC or small gateway directly on the machine or local cell. - The device runs a connector that speaks controller-native protocols (Fanuc Focas, Siemens ShopMill/TIA, Haas USB/serial) and normalizes the data to MTConnect or OPC UA. - Pros: low latency, works offline with local buffering, high fidelity. - Cons: per-machine hardware cost, local maintenance. ### Gateway + middleware approach (aggregate and normalize) - Gateways collect raw signals from several machines, normalize events in middleware, and forward to ERP/MES via message bus (MQTT, AMQP) or REST. - Pros: central visibility, easier schema management across heterogeneous controllers. - Cons: single point of failure unless redundant; higher initial setup for middleware. ### Direct integration using standards (MTConnect / OPC UA) - Use MTConnect or OPC UA agents on machines to publish normalized streams; ERP/MES consumes these endpoints. - Pros: standardized data models, easier vendor interoperability, growing ecosystem. - Cons: requires controller/agent support or protocol converters; some controllers need plugins. | Architecture | Latency | Data fidelity | Installation complexity | Cost | Security | Scalability | Offline behavior | | --- | --- | --- | --- | --- | --- | --- | --- | | Edge connector (per machine) | Low (ms–s) | High | Medium | Medium–High | High (local control) | High (scale per machine) | Buffers locally | | Gateway + middleware | Medium (s) | High (after normalization) | High | Medium–High | High (network controls) | High (centralized) | Gateway buffering | | MTConnect/OPC UA direct | Low–Medium | Standardized | Medium | Low–Medium | High (TLS, certs) | High | Depends on agent | Decision flow: pilot 1–3 machines with an edge connector if you need immediate low-latency events and offline buffering; choose gateway + middleware if managing many heterogeneous controllers; favor MTConnect/OPC UA for longer-term vendor interoperability. For low-cost pilot options, see our [connect machines for free](https://www.jitbase.com/connect-your-machines-for-free-v2) guide. Industry standards and vendor pages are useful background: read about [MTConnect standards and adopters](https://www.mtconnect.org/), and the [OPC Foundation's overview of OPC UA](https://opcfoundation.org/about/opc-technologies/opc-ua/) to compare protocol capabilities. ## Extracting accurate cycle and standard times from CNC programs Accurate cycle and standard times can be derived in multiple ways. Two main methods are static program parsing and dynamic telemetry analysis. Use both where possible and validate with time studies. ### Parsing G-code and detecting cycle boundaries Static parsing looks for program markers and canned cycles. Key indicators: - Program end: M30, M99 - Spindle commands: M03 (start), M05 (stop) - Feed commands: F followed by feed rate - Canned cycles: G81–G89 represent drilling cycles - Comment tags and program headers often include job names and revision numbers Example G-code cues: - A block with M30 typically signifies the end of a part cycle. - G80 followed by an M05 and M09 may indicate spindle and coolant off, which is a logical boundary. Static parsing is lightweight but misses runtime variations (feed overrides, operator holds). Use it to estimate baseline cycle times and tag program names. Refer to open G-code references for common codes: the [LinuxCNC G-code documentation](https://linuxcnc.org/docs/2.8/html/gcode/) provides a practical reference for parsing behaviors. **Extract cycle times directly from CNC programs.** Analyze G-code programs and machine data to generate accurate cycle times for production planning and quoting. [See how production monitoring works](https://www.jitbase.com/production-monitoring) ### Using spindle/axis telemetry, spindle load, and probes Dynamic telemetry tracks actual machine behavior: - Spindle on/off events and spindle load (amperage) indicate active cutting windows. - Axis motion (X/Y/Z moves) shows when the tool is moving; combining axis motion with spindle-on windows refines cycle boundaries. - Probes trigger events for touch-off or in-process inspection. Hardware sensors that help include: - Current clamps for spindle power/load - Encoder/axis movement telemetry via controller APIs - Proximity sensors for pallet presence or door open/close Dynamic telemetry often gives the closest match to actual cycle time. For some shops, adding a spindle current clamp reduces cycle time measurement error from program parsing variance. ### Validating automated times with time studies Automated cycle time capture should be validated with short time studies: - Select 10–20 consecutive cycles on representative programs. - Compare automated cycle\_time\_ms to stopwatch or video-based measurements. - Target tolerance: within 5–10% for standard time capture. If variance exceeds this, check signal noise, program naming consistency, or missed tool-change events. When mixed-program variability is present (same program but different fixture setups), use program + fixture identifiers or operator-provided routing codes to separate time-series. Reconcile differences by splitting program instances in analytics. ## Implementation roadmap: pilot to plantwide deployment and ERP/MES integration patterns ### Designing a pilot: scope, duration, success criteria Recommended pilot timeline: - Discovery and baseline (2 weeks): intervention logging, identify 1–3 candidate machines, map controller types (Fanuc, Siemens, Haas). - Pilot installation (1–4 weeks): install edge connectors or gateways, enable MTConnect/OPC UA agents, configure event models. - Validation and KPI tuning (2–6 weeks): run pilot under production traffic, refine event rules, validate cycle times with time studies. - Phased rollout: expand by cell or machine type after successful pilot. Define success criteria before installation: - Manual intervention rate reduced by X% (example: 50% fewer manual stops for tool changes). - Cycle time variance reduction to within 5–10% of time-study results. - Event delivery success to ERP/MES ≥ 98% for business hours. - No negative production impact from automation during pilot. A useful case study for scaled outcomes is our [25% more utilization case study](https://www.jitbase.com/case-study-25-more-machine-utilization-leesta). ### Data mapping and event models for ERP/MES Design a concise event model for each event type. Sample JSON payload for a cycle end event: ``` { "event_id": "EVT-000123", "machine_id": "CELL-1-AXIS-01", "timestamp": "2026-03-01T14:22:35.123Z", "event_type": "cycle_end", "program_name": "P00042_REV3", "cycle_time_ms": 91234, "operator_id": "OP123", "part_id": "PART-789", "tool_change_count": 2, "notes": "" } ``` Fields to include: - event\_id, machine\_id, timestamp, event\_type (standardized), program\_name, cycle\_time\_ms - Optional: operator\_id, shift\_id, part\_id, fixture\_id, spindle\_load, coolant\_state Integration patterns: - Push events to ERP via REST with acknowledgements for reconciliation. - Publish events to a message bus (MQTT/AMQP/Kafka) and have ERP/MES subscribe. - Have MES poll an intermediary normalized API if push is not possible. Ensure idempotency (include a unique event\_id or sequence) to prevent duplicate processing. Use simple retry and dead-letter queue patterns to handle temporary ERP downtime. Part count events are worth mapping early: the guide to [automatic part counting](https://www.jitbase.com/blog/part-counting-cnc-shops) shows how to validate them before they reach the ERP. ### Rollout phases, training, and change management Rollouts fail when operators are not part of the change. Train operators on: - What events are automated and what manual confirmations remain. - How to tag exceptions (scrap, rework) so analytics remain accurate. - How to read the local connector status and basic troubleshooting. Adopt phased rollout: complete one cell before starting the next. Keep a "buddy" expert (maintenance or controls engineer) available during the first shifts after rollout. ## Key metrics, dashboards, ROI and common pitfalls ### Essential KPIs to track (OEE/TRS, cycle time variance, manual intervention rate) Track these metrics: - OEE/TRS components: Availability, Performance, Quality. See OEE calculation guidance at [OEE basics and calculation](https://www.oee.com/calculating-oee/). - Cycle time variance by program: standard deviation and 95th percentile. - Manual intervention rate: number of manual confirmations per 100 cycles. - Event delivery success: percent of events received and reconciled by ERP/MES. - Exception rate: events requiring manual correction in ERP. **Bring ERP data to life on the shop floor.** Connect ERP work orders with machine data and real cycle times to gain accurate visibility into production progress and capacity. [Discover how to bring ERP data to life](https://www.jitbase.com/bring-erp-data-to-life) ### Sample dashboard and data requirements Dashboard widgets (minimum): - Live machine state map (running, idle, fault) - Average cycle time by program (30min, 8hr, 7-day views) - Manual intervention count and trend - Exception list (unmatched events, failed deliveries) - Utilization by shift and operator Data needed: timestamped events, program identifiers, operator tags, cycle times, event acknowledgements from ERP. For automated detection of downtime and state, refer to our guidance on [automated downtime detection](https://www.jitbase.com/blog/automated-downtime-detection-cnc-setup). ### ROI calculation inputs Estimate ROI from: - Labor cost saved (reduced operator minutes per shift × hourly rate) - Machine runtime recovered (hours recovered × machine hourly cost) - Reduced scrap and rework rates (percent reduction × cost per scrap) - Improved lead time and increased throughput enabling new orders Example: A shop with 10 machines recovering 0.5 productive hours per machine per day at $80/hr machine cost nets $400/day, or about $100k/year in recovered runtime, then compare against connector/hardware and integration costs for payback. **Estimate the return before you start a pilot.** Size the gain from recovered machine time and fewer manual interventions for your own shop. [Estimate your return on investment](https://www.jitbase.com/return-on-investment) ### Troubleshooting checklist and common failure modes Common problems and fixes: - Noisy signals: verify sensor wiring and add filtering, debounce events. - Inconsistent program naming: enforce naming rules at CAM stage or use program checksums. - Operator tagging errors: add simple operator ID capture at HMI or badge tap. - Network latency or packet loss: add local buffering and ACKs for event delivery. - Duplicate events: add unique event IDs and idempotent processing. Follow data governance: canonical program name, event type dictionary, and error reconciliation procedures. For OEE/TRS formulas and computation tips, review our guide to [calculate OEE](https://www.jitbase.com/blog/implement-real-time-oee-dashboards-small-cnc-shops). Key quick wins: - Automate cycle start/stop logging first: it provides immediate scheduling benefits. - Use spindle-on plus axis-motion as the primary cycle boundary for mixed programs. - Start with edge-device buffering to avoid event loss during network outages. ## The Bottom Line Automating manual interventions and streaming CNC events into ERP/MES recovers machine hours, reduces operator load, and yields reliable cycle-time data to drive better planning. Run a short discovery audit, pick a 1–3 machine pilot, and set clear KPIs (manual stop reduction, cycle time accuracy, event reconciliation) to validate value before scaling. ## Frequently Asked Questions ### How long does a pilot typically take and what resources are needed? A minimal pilot usually runs 6 to 12 weeks: about two weeks of discovery and baseline logging, one to four weeks for connector installation, and two to six weeks for validation and KPI tuning. The timeline stretches when the shop has several controller brands, when network access needs IT approval, or when program naming is inconsistent and has to be cleaned up first. Resources are modest: a controls or automation engineer, an IT contact for network and security, one shop-floor technician, and one to three machines that represent your fleet. Budget for an edge connector (per machine or per cell), any extra sensors such as a current clamp or proximity switch, and minor PLC or I/O work. ### Can legacy CNC machines be integrated? Yes. Legacy machines without native MTConnect or OPC UA can be integrated with protocol converters, discrete I/O sensing (spindle current, axis movement), or controller-specific adapters such as Fanuc FOCAS or a Siemens OPC server. The right approach depends on the ports your controller exposes and on how much event fidelity you need. Start with spindle-on and axis-motion signals, which give reliable cycle detection even on older controls. Once those events are stable, add program parsing or deeper controller integration to capture program names, offsets, and tool changes. Check whether your controller is already on a supported-machines list before you buy any hardware. ### How do I ensure my ERP accepts streamed events? Agree with the ERP or MES owner on the payload schema and the delivery pattern before building anything: push via REST, subscription on a message bus, or a file drop. Include a unique event ID in every message so processing stays idempotent, and require an acknowledgement so the sender knows which events to retry. Run a small integration test with sample payloads, then keep reconciliation logs available so planners can confirm that streamed events match the ERP records. Track the event delivery success rate during the pilot and investigate every unmatched event, since a few recurring causes (duplicate IDs, unknown program names, late timestamps) usually explain most failures. ### What security measures should be used when streaming events? Separate the machine network from the office network with a dedicated VLAN, use mutual TLS for MQTT and REST endpoints, rely on certificate-based authentication for agents, and keep the number of open ports to the minimum. Encrypt data in transit and apply role-based access control on any middleware. Work with your IT team on firewall rules and monitoring so the machine network stays protected but remains reachable for controlled event streaming. Prefer outbound-only connections from the edge device, document who can change connector settings, and review access logs periodically as part of your normal IT routine. ### How do I validate cycle time accuracy after automation? Run a focused time study: record 10 to 20 consecutive cycles on representative programs and compare the automated cycle\_time\_ms with stopwatch or video measurements. Calculate the mean error and the standard deviation, and aim for an accuracy within 5 to 10% of the validated time study. If the error is higher, check the signal choice (spindle current versus spindle on), sensor placement, and program variability. Use program and fixture tags to separate mixed populations, and repeat the study after any change to the capture rules, because a small rule change can shift the measured times. #### SHARE [mailto:email@example.com?subject=Mail%20from%20our%20Website](mailto:email@example.com?subject=Mail%20from%20our%20Website) ## Related Articles [![Production operator walking along CNC machines with prepared tooling fixtures, illustrating reduced manual interventions, automated workflows, and streamlined shop-floor operations with JITbase.](https://www.jitbase.com/hs-fs/hubfs/blog-images/reduce-manual-interventions-shop-floor-checklist-hero.jpg?width=352&name=reduce-manual-interventions-shop-floor-checklist-hero.jpg)](https://www.jitbase.com/blog/reduce-manual-interventions-shop-floor-checklist) [Production Management](https://www.jitbase.com/blog/tag/production-management) ### [Checklist: How to Reduce Manual Interventions on the Shop Floor to Free Operator Time and Increase Throughput](https://www.jitbase.com/blog/reduce-manual-interventions-shop-floor-checklist) Read more [![CNC machine connected to an ERP dashboard: shop-floor data integration with JITbase](https://www.jitbase.com/hs-fs/hubfs/blog-images/integrate-shop-floor-data-erp-without-disruption-hero.jpg?width=352&name=integrate-shop-floor-data-erp-without-disruption-hero.jpg)](https://www.jitbase.com/blog/integrate-shop-floor-data-erp-without-disruption) [Production Management](https://www.jitbase.com/blog/tag/production-management) ### [7 Practical Steps to Integrate Shop-Floor Data with Your ERP/MES Without Disrupting Production](https://www.jitbase.com/blog/integrate-shop-floor-data-erp-without-disruption) Read more [![Technician connecting a CNC machine to a system using an edge device and Ethernet cable, illustrating machine data integration with ERP and MES.](https://www.jitbase.com/hs-fs/hubfs/blog-images/integrating-cnc-data-erp-mes-hero.jpg?width=352&name=integrating-cnc-data-erp-mes-hero.jpg)](https://www.jitbase.com/blog/integrating-cnc-data-erp-mes) [Production Management](https://www.jitbase.com/blog/tag/production-management) ### [Step-by-Step Guide to Integrating CNC Machine Data with ERP/MES for Real-Time Throughput Gains](https://www.jitbase.com/blog/integrating-cnc-data-erp-mes) Read more [![Engineer reviewing a shop-floor checklist while CNC machines exchange data, illustrating MES and ERP integration and structured production workflows with JITbase.](https://www.jitbase.com/hs-fs/hubfs/blog-images/integrate-shop-floor-data-erp-mes-checklist-template-hero.jpg?width=352&name=integrate-shop-floor-data-erp-mes-checklist-template-hero.jpg)](https://www.jitbase.com/blog/integrate-shop-floor-data-erp-mes-checklist-template) [Production Management](https://www.jitbase.com/blog/tag/production-management) ### [Checklist + Template: Integrate Shop-Floor Cycle Data with ERP/MES to Boost Throughput Without Adding Staff](https://www.jitbase.com/blog/integrate-shop-floor-data-erp-mes-checklist-template) Read more [![JITbase block logo](https://www.jitbase.com/hs-fs/hubfs/eca01011d7169e8c6395a8d7eebe802d.webp?width=317&height=158&name=eca01011d7169e8c6395a8d7eebe802d.webp)](https://www.jitbase.com) ![image-2](https://www.jitbase.com/hs-fs/hubfs/image-2.webp?width=156&height=78&name=image-2.webp) Solutions - 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[Privacy Policy](https://www.jitbase.com/privacy-policy) ```json { "@context" : "https://schema.org", "@type" : "BlogPosting", "author" : { "@type" : "Person", "name" : "Judicael Deguenon", "url" : "https://www.jitbase.com/blog/author/judicael-deguenon" }, "dateModified" : "2026-10-15T15:35:14.371Z", "datePublished" : "2026-03-19T15:27:15.000Z", "headline" : "Implementation Guide: Automate Manual Interventions on CNC Machines and Stream Events to ERP/MES", "image" : [ "https://www.jitbase.com/hubfs/blog-images/automate-manual-interventions-cnc-erp-mes-integration-hero.jpg" ], "mainEntityOfPage" : { "@id" : "https://www.jitbase.com/blog/automate-manual-interventions-cnc-erp-mes-integration", "@type" : "WebPage" }, "publisher" : { "@type" : "Organization", "logo" : { "@type" : "ImageObject", "url" : "https://www.jitbase.com/hubfs/logo%20jitbase%201.svg" }, "name" : "JITbase" } } ``` ```json { "@context" : "https://schema.org", "@type" : "FAQPage", "mainEntity" : [ { "@type" : "Question", "acceptedAnswer" : { "@type" : "Answer", "text" : "A minimal pilot usually runs 6 to 12 weeks: about two weeks of discovery and baseline logging, one to four weeks for connector installation, and two to six weeks for validation and KPI tuning. The timeline stretches when the shop has several controller brands, when network access needs IT approval, or when program naming is inconsistent and has to be cleaned up first. Resources are modest: a controls or automation engineer, an IT contact for network and security, one shop-floor technician, and one to three machines that represent your fleet. Budget for an edge connector (per machine or per cell), any extra sensors such as a current clamp or proximity switch, and minor PLC or I/O work." }, "name" : "How long does a pilot typically take and what resources are needed?" }, { "@type" : "Question", "acceptedAnswer" : { "@type" : "Answer", "text" : "Yes. Legacy machines without native MTConnect or OPC UA can be integrated with protocol converters, discrete I/O sensing (spindle current, axis movement), or controller-specific adapters such as Fanuc FOCAS or a Siemens OPC server. The right approach depends on the ports your controller exposes and on how much event fidelity you need. Start with spindle-on and axis-motion signals, which give reliable cycle detection even on older controls. Once those events are stable, add program parsing or deeper controller integration to capture program names, offsets, and tool changes. Check whether your controller is already on a supported-machines list before you buy any hardware." }, "name" : "Can legacy CNC machines be integrated?" }, { "@type" : "Question", "acceptedAnswer" : { "@type" : "Answer", "text" : "Agree with the ERP or MES owner on the payload schema and the delivery pattern before building anything: push via REST, subscription on a message bus, or a file drop. Include a unique event ID in every message so processing stays idempotent, and require an acknowledgement so the sender knows which events to retry. Run a small integration test with sample payloads, then keep reconciliation logs available so planners can confirm that streamed events match the ERP records. Track the event delivery success rate during the pilot and investigate every unmatched event, since a few recurring causes (duplicate IDs, unknown program names, late timestamps) usually explain most failures." }, "name" : "How do I ensure my ERP accepts streamed events?" }, { "@type" : "Question", "acceptedAnswer" : { "@type" : "Answer", "text" : "Separate the machine network from the office network with a dedicated VLAN, use mutual TLS for MQTT and REST endpoints, rely on certificate-based authentication for agents, and keep the number of open ports to the minimum. Encrypt data in transit and apply role-based access control on any middleware. Work with your IT team on firewall rules and monitoring so the machine network stays protected but remains reachable for controlled event streaming. Prefer outbound-only connections from the edge device, document who can change connector settings, and review access logs periodically as part of your normal IT routine." }, "name" : "What security measures should be used when streaming events?" }, { "@type" : "Question", "acceptedAnswer" : { "@type" : "Answer", "text" : "Run a focused time study: record 10 to 20 consecutive cycles on representative programs and compare the automated cycle_time_ms with stopwatch or video measurements. Calculate the mean error and the standard deviation, and aim for an accuracy within 5 to 10% of the validated time study. If the error is higher, check the signal choice (spindle current versus spindle on), sensor placement, and program variability. Use program and fixture tags to separate mixed populations, and repeat the study after any change to the capture rules, because a small rule change can shift the measured times." }, "name" : "How do I validate cycle time accuracy after automation?" } ] } ```